Recommender and method of providing a recommendation of content therefor

ABSTRACT

The invention relates to a system for recommending content items. A user profile processor ( 113 ) determines ( 201 ) a user preference profile for a user for different categories of content. When a content item is received ( 203 ), a recommender processor ( 111 ) determines ( 205 ) if a first content item correlates with the user preference profile. If there is such an associative correspondence, the content item is recommended ( 206 ) to the user. If not, the recommender processor ( 111 ) determines ( 211 ) if the received content item comprises at least a first characteristic having an associative correspondence to at least a second characteristic of a second content item having a high user preference. If there is such an associative correspondence, the received content item is recommended to the user. The associative correspondence may be determined to exist if textual descriptions for the two content items comprise similar words. The invention allows an increased diversity in recommendations and is particularly suitable for a Private Video Recorder.

FIELD OF THE INVENTION

The invention relates to a recommender and a method of providing arecommendation of content therefor and in particular to a recommendersuitable for a Private Video Recorder.

BACKGROUND OF THE INVENTION

In recent years, the accessibility to and provision of information andcontent such as TV programmes, film, music and books, etc. haveincreased explosively. The information and content may today be providedfrom many different sources, and the variety and availability of contenthas increased substantially.

For example, the number of available television channels in mostcountries has increased substantially in the last decade, and in manycountries, viewers can receive tens or even hundreds of different TVchannels. The TV channels are further provided from differentbroadcasters and sources and are communicated through a variety of mediaincluding terrestrial radio broadcasts, cable distribution or satellitebroadcasts. Similarly, the number of available radio channels hasincreased explosively and are provided through different media such assatellite broadcasts, digital terrestrial broadcasts, cable distributionor even through the Internet.

As the available content has increased substantially, it has becomeincreasingly difficult for a user to find and select the specificcontent that he is most interested in. Obtaining information of thetotal amount of content available and filtering this in order to selecta desired content item is a very time-consuming and cumbersome process.In addition to finding the appropriate content item, the user furtherneeds to determine from which source and at which time the desiredcontent item is available.

In order to facilitate content selection, and to filter the availablecontent to provide a suitable selection for a given user, recommendershave been developed, which are able to monitor the available contentand, in response to a user profile, recommend content consideredspecifically suited for the user.

One area where recommenders have been implemented is in Private VideoRecorders (PVRs). A typical PVR comprises a hard disk for recordingcontent items such as TV programmes. The PVR further comprises arecommender, which records and recommends TV programmes to the user inaccordance with a user profile. The user profile is built up over timeto match the user's viewing habits, and the profile is specificallygenerated from specific user input related to the preference for a givenprogramme as well as from detecting which programmes are selected forviewing by the user of the PVR.

Although conventional recommenders may facilitate content selection andprovide recommendations, further improvement of the functionalityprovided would be advantageous.

For example, as the user profile is built up over a significant time, ittends to become relatively static, and modifications and updates canonly gradually be incorporated. Furthermore, the user profile isdetermined in response to the user's preference for selected programmes.However, as the user typically selects items recommended to him from thecontent, the update information available for the user preferenceprofile is typically limited to content already recommended. Thus, thecontent recommendation will tend to become more and more narrow withonly content of a limited range being recommended. Thus, over time, thevariety of recommendations becomes severely limited in conventionalrecommenders.

Hence, a system for an improved recommender would be advantageous, andespecially a system providing increased flexibility and/or variety ofrecommendations.

OBJECT AND SUMMARY OF THE INVENTION

Accordingly, the invention seeks to provide an improved system for arecommender and/or to mitigate, alleviate or eliminate one or more ofthe above-mentioned disadvantages singly or in any combination.

According to a first aspect of the invention, a method of providing arecommendation of content to a user comprises the steps of: determininga user preference profile for a user; determining if a first contentitem correlates with the user preference profile so as to have a highpreference value; and if the first content item has a high preferencevalue recommending it to a user; and if the first content item does nothave a high preference value: determining if the first content itemcomprises at least a first characteristic having an associativecorrespondence to at least a second characteristic of a second contentitem having a high user preference and recommending it to the user onlyif there is such an associative correspondence.

Hence, an increased variety may be introduced in the recommendations ascontent items not specifically matching the current user preferenceprofile may be recommended to the user. However, these content items arenot randomly selected but may be selected in response to an associativecorrespondence between a first characteristic of the first content itemand a second characteristic of a second preferred content item. Hence,the recommended content items will be related to content items known tohave a high preference. Consequently, content items may be recommendedon the basis of a relatively loose association with other preferredcontent items. This allows alternative content items that do not closelymatch the user preference profile to be recommended while increasing theprobability that the recommended content item is suited for the user.Hence, the invention provides an efficient method of expanding andincreasing the variety of recommendations.

The increased variety may further be used to update the user preferenceprofile such that the preference information may be expanded into, forexample, new categories of content. Thus a widening mechanism may beintroduced to the user preference profile thereby opposing the narrowingeffect caused by a limited recommendation of content for preferenceevaluation. The content items may be, for example, TV programmes, videoclips, audio clips, radio programmes, music clips, multimedia clips orany other suitable content items.

According to a feature of the invention, the first content item isrecommended to the user if only a single associative correspondencebetween the first characteristic and the second characteristic isdetermined. Specifically, a single associative correspondence betweenthe first and the second content item may be sufficient to result in arecommendation. This allows for increased diversity of content items tobe recommended. Specifically, it may be required that no more than oneassociative correspondence is determined in order for the content itemto be recommended. This will allow that some of the content itemsrecommended are significantly different than the currently preferredcontent items.

According to another feature of the invention, the associativecorrespondence is determined only for a single first and secondcharacteristic. This may provide for a recommendation of a content itemwhich is correlated with one or more preferred content items but at thesame time has a high probability of not being too closely related to thepreferred content items.

According to another feature of the invention, the method furthercomprises the step of determining a user preference for the firstcontent item recommended from the associative correspondence andupdating the user preference profile in response to the user preference.This allows the user preference profile to be updated with preferencevalues for content items that currently have no or low preferenceratings. Hence, the user preference profile may be updated to includepositive preferences for new categories of content, thereby allowing thefuture recommendations to become more varied and diversified. Theincreased variation is thus not limited to the current recommendationsbut may be achieved for future recommendations.

According to another feature of the invention, the first characteristicis a first content description characteristic of the first content itemand the second characteristic is a second content descriptioncharacteristic of the second content item. Any suitable characteristicor attribute of the content descriptions, such as meta-data, may beused. This provides for the association between the first and secondcontent items to be based on the content characteristics, therebyimproving the probability that the first content item has a content thatsuits the user.

According to another feature of the invention, the first contentdescription characteristic is derived from a first textual descriptionassociated with the first content item and the second contentdescription characteristic is derived from a second textual descriptionassociated with the second content item. Text-based content descriptionis typically prevalent for broadcast content. It is furthermore easy toaccess and process. The use of text-based content descriptions thereforeprovides a suitable and easy to implement basis for determining anassociative correspondence.

According to another feature of the invention, the associativecorrespondence is determined in response to an identification of acorrespondence between at least one word of the first textualdescription and at least one word of the second textual description.This provides a simple yet highly efficient way of determining theassociative correspondence between content items.

According to another feature of the invention, the correspondence isdetermined in response to the at least one word of the first textualdescription having a similar meaning as the at least one word of thesecond textual description. This provides for only simple processing tobe required to determine an associative correspondence, yet allowscontent items to be recommended that differ from currently preferredcontent while having a reasonable probability of being selected and/orpreferred by the user.

According to another feature of the invention, the correspondence isdetermined in response to at the least one word of the first textualdescription having an associative word correspondence to the at leastone word of the second textual description, the associative wordcorrespondence being determined from a database of word associations.Hence, the associative correspondence may not (or not exclusively) bedetermined by words having identical or similar meanings but may also bedetermined in response to words being associated with each other. A listof associations between words may be stored in a data base and accessedto determine the associative correspondence.

According to another feature of the invention, the associativecorrespondence is determined in response to word combinations of atleast one of the first and second textual content description. This mayprovide an increased flexibility and accuracy of determining theassociative correspondence between the first and the secondcharacteristic.

According to another feature of the invention, at least one of the firstand second characteristics is determined from a content analysis of thecontent item. Specifically, the content analysis may comprise a contentitem video image analysis, such as a content item video object analysis,and/or a content item audio analysis. This allows the associativecorrespondence to be determined on the basis of only the content itemswithout requiring any additional information.

According to another feature of the invention, wherein at least one ofthe first and second characteristic is determined from a content itembroadcast channel, the first and second characteristics may beassociated with characteristics of the first and second content items inrelation to the content item broadcast channel. This may, for example,include a time of broadcast of the content item. This provides anadditional or alternative method of determining an associativecorrespondence allowing the recommendation of probably suitable butcurrently non-preferred content items.

According to another feature of the invention, the step of determiningthe associative correspondence comprises determining a plurality ofassociative correspondences between a plurality of characteristics ofthe first content item and a plurality of characteristics of the secondcontent item. This allows the probability of the first content item tobe suited for the user to be increased.

According to another feature of the invention, the associativecorrespondence is further determined in response to a previousassociative correspondence between content items. This allows the systemto learn from previous behaviour. Specifically, if some types ofassociative correspondence have been found to result in content beingrecommended that has received high user preference indications, thisassociative correspondence may be used increasingly in the future.Hence, it provides an increased probability that recommended contentitems are suitable for the user.

According to another feature of the invention, at least one of the firstand second characteristics is chosen from the group of: an actor; acharacter played by an actor; and a location. These characteristicsprovide a suitable basis for determining associative correspondencesthat result in diverse recommendations, yet with a reasonableprobability of suiting the user.

According to a second aspect of the invention, there is provided arecommender for providing a recommendation of content to a user, therecommender comprising: a user profile processor for determining a userpreference profile for a user; a recommender processor for determiningif a first content item correlates with the user preference profile soas to have a high preference value; and if the first content item has ahigh preference value recommending it to a user, and if the firstcontent item does not have a high preference value: determining if thefirst content item comprises at least a first characteristic having anassociative correspondence to at least a second characteristic of asecond content item having a high user preference and recommending it tothe user only if there is such an associative correspondence.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment of the invention will be described, by way of exampleonly, with reference to the drawings, in which

FIG. 1 is an illustration of a private video recorder comprising arecommender in accordance with an embodiment of the invention; and

FIG. 2 is an illustration of a method of providing a recommendation ofcontent in accordance with an embodiment of the invention.

DESCRIPTION OF PREFERRED EMBODIMENTS

The following description focuses on an embodiment of the inventionapplicable to a Private Video Recorder (PVR) comprising a recommender.However, it will be apparent that the invention is not limited to thisapplication but may be applied to many other applications includingrecommenders for radio programme content or Internet content.

FIG. 1 is an illustration of a private video recorder (PVR) 101comprising a recommender in accordance with an embodiment of theinvention. The PVR 101 comprises a content receiver 103. The contentreceiver 103 receives content items from one or more suitable contentitem sources. In the preferred embodiment, the content receiver 103mainly receives content by way of TV programmes broadcast in a suitableway.

Furthermore, in the preferred embodiment, the content receiver iscapable of receiving content from a plurality of various contentsources. Thus, the content receiver 103 receives content items in theform of video, audio and multimedia clips and programmes. Specifically,TV programmes are received from terrestrial radio broadcasts as well asfrom a digital cable connection. Likewise, radio programmes are receivedfrom conventional analogue radio transmissions as well as from digitalradio broadcasts received through a cable connection. The contentreceiver 103 capable of receiving a plurality of content items fromvarious sources may simply be implemented as the combination of aplurality of independent content receiver elements, where each elementis dedicated to receiving content items of a specific nature from aspecific source.

The received content items are converted to suitable digital formats andstored in a content memory 105 together with information associated withthe content items. Specifically, a content item may be received directlyin a suitable format, such as an MPEG 2 format for a video transmission,and in this case no conversion is required.

The PVR 101 further comprises a user interface 107 for displayingcontent items, control information and for receiving user input.Specifically, the user interface 107 comprises a display such as e.g. avideo monitor or a TV. In the preferred embodiment, the user input isreceived by using a remote control communicating with the user interface107. Hence, the user interface is operable to display variousinformation to the user and to receive user inputs. Specifically, theuser interface may display a list of content items, and a user mayselect one of these through a suitable activation of the remote control.

The PVR additionally comprises a content presenter 109, which is coupledto the content memory 105 and the user interface 107. In response to aselection of a content item, the content presenter 109 is operable toretrieve the stored content from the content memory 105 and present itto the user via the user interface 107.

Furthermore, the PVR 101 comprises a recommender processor 111 coupledto the content receiver 103, the content presenter 109, the userinterface 107 and possibly the content memory 105. The recommenderprocessor 111 is coupled to a user profile processor 113, which isoperable to generate and maintain a user preference profile for a userof the PVR 101.

In the preferred embodiment, the recommender processor 111 detects whichcontent items are presented by the content presenter 109. It furthermoredetermines a user preference for the content items through a specificuser preference indication received through the user interface 107.Additionally or alternatively, the user preference indication may bereceived through indirect measures. These indirect measures includedetection of, for example, how many times a given content item iswatched, whether it is watched in full or only partly, etc.

When the recommender processor 111 detects that a given content item ispresented to the user, it retrieves the associated information from thecontent memory 105. The user preference is correlated with theinformation for the content item, and specifically with the category ofthe content item, in order to derive information of the user'spreference for this category of content item.

This information is forwarded to the user profile processor 113, whichthrough receiving a plurality of such indications builds up knowledge ofthe user's preferences for different categories and types of content.This knowledge is contained in a user preference profile, and the PVR101 comprises a user preference profile memory 115 for storing the userpreference profile. The user preference profile memory 115 is coupled tothe user profile processor 113.

FIG. 2 is an illustration of a method of providing a recommendation ofcontent in accordance with an embodiment of the invention. The methodmay be applicable to the PVR of FIG. 1, and will hereinafter bedescribed with reference thereto.

In step 201, a user preference profile is determined. In the preferredembodiment, the user preference profile is determined in response toprevious user selections. Hence, specifically a user preference profileis generated when the PVR 101 is first initiated and is then stored inthe user preference profile memory 113. The user preference profile iscontinually updated as the PVR 101 is used, and becomes increasinglyaccurate and specific as more and more information is determined. Thedetermination of the user preference profile of step 201 may comprisethe process of generating a new user preference profile. However, in thepreferred embodiment, the determination of step 201 comprises therecommender processor 111 determining the user preference profile simplyby accessing the information stored in the user preference profilememory 113. Hence, the determination preferably simply consists inretrieving or accessing some or all information of the user preferenceprofile stored in the user preference profile memory 113.

In step 203, it is determined if a new content item has been received.The step is repeated until a content item is received. When a firstcontent item is received by the content receiver 103, it is stored inthe content memory 105. In addition, content information is fed to orextracted by the recommender processor 111.

When the first content item has been received, the method continues instep 205 wherein it is detected if the first content item correlateswith the user preference profile so as to have a high preference value,and specifically in the preferred embodiment, whether it matches theuser's current user preference profile. The determination is based onthe content information determined in step 203. If the first contentitem does match the user preference profile, the method continues instep 206 by the recommender processor 111 recommending the content itemto the user. The method then returns to step 203.

If the first content item does not match the user preference profile,the method continues in step 207. In step 207, one or morecharacteristics associated with the first content item is extracted byor fed to the recommender processor 111. The first characteristic may beany suitable characteristic, but in the preferred embodiment itcomprises information related to the content of the first content item.Specifically, the first characteristic may comprise one or more suitablecontent description indicators. Typically, the first characteristic is aspecific parameter or characteristic related to a specific attribute ofthe content of the content item. For example, if the content item is avideo programme such as a film, the first characteristic may relate toan actor in the film, to a specific character played by an actor or to aspecific location included in the film. Thus as a specific example, thefirst characteristic may relate to the main role being played by aspecific actor or to the character played by a specific actor. The firstcharacteristic may further comprise a plurality of different attributes.A specific example of a first characteristic is information that thefilm includes Arnold Schwarzenegger playing a robot in a futuremetropolis.

The method continues in step 209 by determining at least a secondcharacteristic of at least a second content item. The secondcharacteristic may be any suitable characteristic including thecharacteristics described in the previous paragraph for the firstcharacteristic. The second characteristic is preferably determined for aspecific second content item which is known to have a high preferencevalue. However, in some embodiments, the second characteristic relatesto more than a single second content item. Specifically, the secondcharacteristic may be determined from a content category of the userpreference profile comprising the second content item and having a highpreference value.

The method continues in step 211, wherein it is determined if the firstcharacteristic has an associative correspondence to at least the secondcharacteristic. Hence, it is determined if there is any connection orrelation between the first and second characteristics. The associationbetween the first and second characteristic may, for example, consist inan attribute of the first characteristic being similar or identical toan attribute of the second characteristic. However, the first and secondcharacteristics need not be of an identical or similar type ofattribute, but the association may be related across different types ofattributes. For example, an associative correspondence may exist betweena specific actor identified in the first characteristic and a specificcharacter identified in the second content item because it is known thatthe actor is associated with this character. Thus, an associativecorrespondence may exist between an identification of Sean Connery inthe first characteristic and an identification of James Bond in thesecond characteristic.

If an associative correspondence is found to exist, the method continuesin step 213 by recommending the first content item. Otherwise, themethod returns to step 203 or step 201. Thus, as a specific example, acontent item comprising Sean Connery as an actor may be recommendedbecause the user preference profile indicates that the user has a highpreference for James Bond-associated content items.

The associative correspondence may be determined in response to morethan just the first and second characteristics, and each of the firstand second characteristics may comprise a plurality of differentinformation elements and/or attributes. However, in the preferredembodiment, the associative correspondence is much more limited than thematching between the content item and the user preference profile.Specifically, the associative correspondence may be based on only onecharacteristic and attribute of the first and second content item, oreven require that only one associative correspondence exists betweenthem. This will ensure that, although the recommended content item isrelated to known preferred content items, this relation is not a closerelationship, and that therefore the recommended content item willdiffer significantly from the preferred content items of the userpreference profile.

Many different methods, rules and/or algorithms can be used to determinethe associative correspondence, and the determination may be based onany suitable determination and nature of the first and secondcharacteristics.

However, in the preferred embodiment, the first characteristic is afirst content description characteristic of the first content item andthe second characteristic is a second content description characteristicof the second content item. Thus both characteristics relate to thecontent of the content items.

Specifically, the first content characteristic may be derived from afirst textual description associated with the first content item, andthe second characteristic may be derived from a second textualdescription associated with the second content item. Thus theassociative correspondence is determined in response to textualdescriptions of the first and second content items. The textdescriptions may be received in any suitable way and form. However, inthe preferred embodiment, the text descriptions of content items arereceived through an Electronic Programme Guide (EPG). The EPG is eitherreceived as part of the received broadcast, or is communicated to thePVR 101 through other means, including from the Internet or through adirect data connection to a central unit.

In one embodiment, the associative correspondence is determined bydetecting if at least one word of the first text description for thefirst content item corresponds to at least one word of the second textdescription for the second content item. The correspondence may bedetermined to exist, if the two text descriptions comprise words thatare identical or similar. In this comparison, many general words such as“is”, “the”, etc. are naturally ignored. For example, the PVR 101 maycomprise a list of words to ignore when making the comparison.

A word similarity test for correspondence will allow content item to berecommended on the basis of only a limited correlation between the firstcontent item and a preferred content item. As a specific example, adescription of the movie “Blue Lagoon” is likely to comprise wordssimilar to what can be found in the description of a documentary abouttropical islands. Thus if the user has rated the movie very highly, thedocumentary about tropical islands may be recommended. As anotherspecific example, both the description of the movie “Magnolia” and themovie “The Player” may comprise the words ‘ . . . intertwine many storylines . . . ’. The recommender may consequently recommend one of thesebased on a high preference for the other.

The words of the different text descriptions need not be identical butmay just be similar or specifically may have similar meaning. Forexample, a correlation may be found between content items having textdescriptions of “rat race” and “burn out” as these are used to describesimilar issues. Furthermore, the correspondence may be determined toexist if an associative word correspondence exists between words of thedifferent text descriptions. Preferably, the associative wordcorrespondence is determined from a database of word associations.Hence, in one embodiment, the recommender incorporates or has access toassociative dictionaries. Hence, the correspondence may be directlydetermined from the titles of the movie “Blue Lagoon” and thedocumentary “Bounty Island documentary” as the associative dictionarywill indicate that the words “Bounty Island” are typically associatedwith “Blue Lagoon”. As another specific example, the movie “Magnolia”may be associated with the movie “Sound of Music” if the description ofthe latter mentions the song “Edelweiss”, in which case bothdescriptions comprise flower names.

In many embodiments, the associative correspondence is determined inresponse to word combinations of the first and second textualdescriptions. For example, the title “Buffy the Vampire Slayer” may beassociated with “Dracula”.

Additionally or alternatively to determining the associativecorrespondence in response to text descriptions, at least one of thefirst and second characteristic is determined from a content analysis ofthe content item. Hence, the associative correspondence is determined inresponse to a content analysis of the first content item, of the secondcontent item or of both content items.

It is within the contemplation of the invention that any suitable methodof content analysis may be used. In the preferred embodiment, thecontent analysis simply comprises extracting meta-data from the contentitem signal indicative of the content of the content item. Thus, thebroadcaster in this embodiment includes data related to the content ofthe video signal in the broadcast. The meta-data may either be embeddedin the content item itself or may be provided as a separate logical orphysical communication channel. Specifically, the meta-data may providecontent description in accordance with the Multimedia ContentDescription Interface, MPEG 7 as standardised by the Moving PicturesExpert Group.

In more advanced embodiments, the content analysis does not require thepresence of dedicated content description but operates directly on thecontent signal itself. In recent years, significant research has beencarried out in the field of content analysis for e.g. video signals andany of the developed methods or algorithms for content analysis may beused without detracting from the invention.

Typically, content analysis is based on detecting specificcharacteristics typical of a category of content. For example, a videocontent item may be detected as relating to a football match by having ahigh average concentration of green colour and a frequent sidewaysmotion. Cartoons are characterised by typically having strong primarycolours, a high level of brightness and sharp colour transitions. Hence,these characteristics are used to determine content information and theassociative correspondence is determined in response to the informationderived. Thus, a received content item may be determined to be acartoon, and if, for example, the user preference profile comprises ahigh preference value for the cartoon “The Simpsons”, the receivedcontent item will be recommended to the user.

Specifically, the content analysis may be a content item video objectanalysis. This is particularly suitable for object recognition and maybe facilitated by using of MPEG-4 or MPEG 7 technology, wherein thecontent provider is required to tag objects with object information. Inthis embodiment, if it is determined that a preferred content itemcomprises a specific car, other content comprising that car may, forexample, be recommended.

For audio content items, the content analysis may e.g. divide musicinto, for example, acoustic music (minimal low frequency rhythm), dancemusic (fast and high volume low frequency rhythm); slow music (slowrhythm), fast music (fast rhythm), etc. This may be used to recommendcontent item characteristics with an associative correspondence to thespecific music category.

Further information on content analysis is generally available to theperson skilled in the art. For example, the articles “Content-BasedMultimedia Indexing and Retrieval” by C. Djeraba, IEEE Multimedia,April-June 2002, Institute of Electrical and Electronic Engineers; “ASurvey on Content-Based Retrieval for Multimedia Databases” by A.Yoshika et al., IEEE Transactions on Knowledge and Data Engineering,vol. 11, No. 1, January/February 1999, Institute of Electrical andElectronic Engineers; “Applications of Video-Content Analysis andRetrieval” by N. Dimitrova et al., IEEE Multimedia, July-September 2002,Institute of Electrical and Electronic Engineers and the referencesincluded therein provide an introduction to content analysis.

Additionally and alternatively, at least one of the first and secondcharacteristics is determined from a content item broadcast channel.Specifically, the first and/or second characteristic may be determinedfrom a relationship between the first and/or second content item and thecontent item broadcast channel. In particular, the relationship maycomprise a time of transmission of the first and/or second content itemon the broadcast channel. This allows associative correspondences to bedetermined in response to, for example, when a content item isbroadcast. Hence, content items may be associated if they are broadcastby the same broadcast channel at the same hour of the day (and thereforepresumably have the same target group).

Preferably, a user preference for the first content item is received,and the user preference profile is updated in preference to this userpreference. Hence, as a content item is suggested that does not matchthe current user preference profile, a user preference for thisalternative content is determined. If the user likes the suggestedcontent, the user preference profile is modified by including a positivepreference value for the content category or categories associated withthe recommended content item. This allows the variety and diversity ofrecommendations to be increased.

Preferably, the associative correspondence is further determined inresponse to a previous associative correspondence between content items.Hence, information is stored of the success of different associativecorrespondences. Thus, if a recommendation was made on the basis of acorrespondence related to the actor in a movie, which resulted in apositive user preference, future associative correspondences will beexamined on the basis of the actors involved in the content items.

The invention can be implemented in any suitable form includinghardware, software, firmware or any combination of them. However, theinvention is preferably implemented as computer software running on oneor more data processors and/or digital signal processors. The elementsand components of an embodiment of the invention may be physically,functionally and logically implemented in any suitable way. Indeed, thefunctionality may be implemented in a single unit, in a plurality ofunits or as part of other functional units. As such, the invention maybe implemented in a single unit or may be physically and functionallydistributed between different units and processors.

Although the present invention has been described in connection with thepreferred embodiment, it is not intended to be limited to the specificform set forth herein. Rather, the scope of the present invention islimited only by the accompanying claims.

1. A method of providing a recommendation of content to a user themethod comprising the steps of: determining (201) a user preferenceprofile for a user; determining (205) if a first content item correlateswith the user preference profile so as to have a high preference value;and if the first content item has a high preference value recommending(206) it to a user; and if the first content item does not have a highpreference value: determining (211) if the first content item comprisesat least a first characteristic having an associative correspondence toat least a second characteristic of a second content item having a highuser preference and recommending it to the user only if there is such anassociative correspondence.
 2. A method as claimed in claim 1, whereinthe first content item is recommended to the user if only a singleassociative correspondence between the first characteristic and thesecond characteristic is determined.
 3. A method as claimed in claim 1,wherein the associative correspondence is determined only for a singlefirst and second characteristic.
 4. A method as claimed in claim 1,further comprising the step of determining a user preference for thefirst content item recommended from the associative correspondence andupdating the user preference profile in response to the user preference.5. A method as claimed in claim 1, wherein the first characteristic is afirst content description characteristic of the first content item andthe second characteristic is a second content description characteristicof the second content item.
 6. A method as claimed in claim 5, whereinthe first content description characteristic is derived from a firsttextual description associated with the first content item and thesecond content description characteristic is derived from a secondtextual description associated with the second content item.
 7. A methodas claimed in claim 6, wherein the associative correspondence isdetermined in response to an identification of a correspondence betweenat least one word of the first textual description and at least one wordof the second textual description.
 8. A method as claimed in claim 7,wherein the correspondence is determined in response to the at least oneword of the first textual description having a similar meaning as the atleast one word of the second textual description.
 9. A method as claimedin claim 7, wherein the correspondence is determined in response to theat least one word of the first textual description having an associativeword correspondence to the at least one word of the second textualdescription, the associative word correspondence being determined from adatabase of word associations.
 10. A method as claimed in claim 7,wherein the associative correspondence is determined in response to wordcombinations of at least one of the first and second textual contentdescriptions.
 11. A method as claimed in claim 1, wherein at least oneof the first and second characteristics is determined from a contentanalysis of the content item.
 12. A method as claimed in claim 11,wherein the content analysis comprises a content item video imageanalysis.
 13. A method as claimed in claim 11, wherein the contentanalysis comprises a content item audio analysis.
 14. A method asclaimed in claim 1, wherein at least one of the first and secondcharacteristic is determined from a content item video object analysis.15. A method as claimed in claim 1, wherein at least one of the firstand second characteristics is determined from a content item broadcastchannel.
 16. A method as claimed in claim 1, wherein the step ofdetermining the associative correspondence comprises determining aplurality of associative correspondences between a plurality ofcharacteristics of the first content item and a plurality ofcharacteristics of the second content item.
 17. A method as claimed inclaim 1, wherein the associative correspondence is further determined inresponse to a previous associative correspondence between content items.18. A method as claimed in claim 1, wherein at least one of the firstand second characteristics is chosen from the group of a. an actor; b. acharacter played by an actor; and c. a location.
 19. A computer programenabling a method to be carried out according to claim
 1. 20. Arecommender for providing a recommendation of content to a user, therecommender comprising: a user profile processor (113) for determining auser preference profile for a user; a recommender processor (111) fordetermining if a first content item correlates with the user preferenceprofile so as to have a high preference value; and if the first contentitem has a high preference value recommending it to a user; and if thefirst content item does not have a high preference value: determining ifthe first content item comprises at least a first characteristic havingan associative correspondence to at least a second characteristic of asecond content item having a high user preference and recommending it tothe user only if there is such an associative correspondence.
 21. Aprivate video recorder (101) comprising a recommender as claimed inclaim 20.